| 1. |
- Abbafati, Cristiana, et al.
(författare)
-
- 2020
-
Tidskriftsartikel (refereegranskat)
|
|
| 2. |
- Desai, Nikita, et al.
(författare)
-
Performance of four computer-coded verbal autopsy methods for cause of death assignment compared with physician coding on 24,000 deaths in low- and middle-income countries
- 2014
-
Ingår i: BMC Medicine. - : BioMed Central. - 1741-7015. ; 12:1, s. 20-
-
Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Physician-coded verbal autopsy (PCVA) is the most widely used method to determine causes of death (CODs) in countries where medical certification of death is uncommon. Computer-coded verbal autopsy (CCVA) methods have been proposed as a faster and cheaper alternative to PCVA, though they have not been widely compared to PCVA or to each other.METHODS: We compared the performance of open-source random forest, open-source tariff method, InterVA-4, and the King-Lu method to PCVA on five datasets comprising over 24,000 verbal autopsies from low- and middle-income countries. Metrics to assess performance were positive predictive value and partial chance-corrected concordance at the individual level, and cause-specific mortality fraction accuracy and cause-specific mortality fraction error at the population level.RESULTS: The positive predictive value for the most probable COD predicted by the four CCVA methods averaged about 43% to 44% across the datasets. The average positive predictive value improved for the top three most probable CODs, with greater improvements for open-source random forest (69%) and open-source tariff method (68%) than for InterVA-4 (62%). The average partial chance-corrected concordance for the most probable COD predicted by the open-source random forest, open-source tariff method and InterVA-4 were 41%, 40% and 41%, respectively, with better results for the top three most probable CODs. Performance generally improved with larger datasets. At the population level, the King-Lu method had the highest average cause-specific mortality fraction accuracy across all five datasets (91%), followed by InterVA-4 (72% across three datasets), open-source random forest (71%) and open-source tariff method (54%).CONCLUSIONS: On an individual level, no single method was able to replicate the physician assignment of COD more than about half the time. At the population level, the King-Lu method was the best method to estimate cause-specific mortality fractions, though it does not assign individual CODs. Future testing should focus on combining different computer-coded verbal autopsy tools, paired with PCVA strengths. This includes using open-source tools applied to larger and varied datasets (especially those including a random sample of deaths drawn from the population), so as to establish the performance for age- and sex-specific CODs.
|
|
| 3. |
- Leitao, Jordana, et al.
(författare)
-
Comparison of physician-certified verbal autopsy with computer-coded verbal autopsy for cause of death assignment in hospitalized patients in low- and middle-income countries : systematic review
- 2014
-
Ingår i: BMC Medicine. - : BioMed Central. - 1741-7015. ; 12:1, s. 22-
-
Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Computer-coded verbal autopsy (CCVA) methods to assign causes of death (CODs) for medically unattended deaths have been proposed as an alternative to physician-certified verbal autopsy (PCVA). We conducted a systematic review of 19 published comparison studies (from 684 evaluated), most of which used hospital-based deaths as the reference standard. We assessed the performance of PCVA and five CCVA methods: Random Forest, Tariff, InterVA, King-Lu, and Simplified Symptom Pattern.METHODS: The reviewed studies assessed methods' performance through various metrics: sensitivity, specificity, and chance-corrected concordance for coding individual deaths, and cause-specific mortality fraction (CSMF) error and CSMF accuracy at the population level. These results were summarized into means, medians, and ranges.RESULTS: The 19 studies ranged from 200 to 50,000 deaths per study (total over 116,000 deaths). Sensitivity of PCVA versus hospital-assigned COD varied widely by cause, but showed consistently high specificity. PCVA and CCVA methods had an overall chance-corrected concordance of about 50% or lower, across all ages and CODs. At the population level, the relative CSMF error between PCVA and hospital-based deaths indicated good performance for most CODs. Random Forest had the best CSMF accuracy performance, followed closely by PCVA and the other CCVA methods, but with lower values for InterVA-3.CONCLUSIONS: There is no single best-performing coding method for verbal autopsies across various studies and metrics. There is little current justification for CCVA to replace PCVA, particularly as physician diagnosis remains the worldwide standard for clinical diagnosis on live patients. Further assessments and large accessible datasets on which to train and test combinations of methods are required, particularly for rural deaths without medical attention.
|
|
| 4. |
- Mehra, Devika, et al.
(författare)
-
Centres of Excellence for Adolescent Health and Development : A Case Study from Uttar Pradesh, India
- 2023
-
Ingår i: International Journal of Environmental Research and Public Health. - : MDPI AG. - 1661-7827 .- 1660-4601. ; 20:4
-
Tidskriftsartikel (refereegranskat)abstract
- Adolescents and young adult comprise a significant proportion of India’s population. Although, this group of the population faces serious challenges to their health and well-being. To promote their health and well-being, Centre of Excellence (CoE) at King George’s Medical University, Lucknow, India, serves as an advanced care facility for 10–24-year-old adolescents and young adult women. This paper reports the socio-demographic characteristics of, and health services availed to adolescents and young adults who are visiting the CoE in Lucknow, India. A total of 6038 beneficiaries received clinical services during June 2018–March 2022. Out of total clinical services, 38.37% counselling and 37.53% referral services were utilised. Menstruation (46.29%), sexual and reproductive (28.19%), nutrition (5.91%), and mental health (1.67%) related problems were highly reported. The age of beneficiaries is classified into three categories, i.e., 10–14, 15–19, and 20–24 years. Prevalence of overweight was highest among adolescents aged 20–24 years compared to other age groups. Other than nutrition, late-adolescent girls (15–19) faced more health problems than their counterparts. The percentage of beneficiaries decreased significantly during and post the COVID-19 period (<0.001). Therefore, age-specific programs are currently needed, and interventions need to be designed accordingly.
|
|
